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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>.AdaBoostRegressor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-ensemble-adaboostregressor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostRegressor</span></code></a></li>
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  <div class="section" id="sklearn-ensemble-adaboostregressor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>.AdaBoostRegressor<a class="headerlink" href="#sklearn-ensemble-adaboostregressor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.ensemble.AdaBoostRegressor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.ensemble.</code><code class="sig-name descname">AdaBoostRegressor</code><span class="sig-paren">(</span><em class="sig-param">base_estimator=None</em>, <em class="sig-param">n_estimators=50</em>, <em class="sig-param">learning_rate=1.0</em>, <em class="sig-param">loss='linear'</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L865"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>An AdaBoost regressor.</p>
<p>An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
regressor on the original dataset and then fits additional copies of the
regressor on the same dataset but where the weights of instances are
adjusted according to the error of the current prediction. As such,
subsequent regressors focus more on difficult cases.</p>
<p>This class implements the algorithm known as AdaBoost.R2 [2].</p>
<p>Read more in the <a class="reference internal" href="../ensemble.html#adaboost"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.14.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>base_estimator</strong><span class="classifier">object, optional (default=None)</span></dt><dd><p>The base estimator from which the boosted ensemble is built.
If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then the base estimator is
<code class="docutils literal notranslate"><span class="pre">DecisionTreeRegressor(max_depth=3)</span></code>.</p>
</dd>
<dt><strong>n_estimators</strong><span class="classifier">integer, optional (default=50)</span></dt><dd><p>The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.</p>
</dd>
<dt><strong>learning_rate</strong><span class="classifier">float, optional (default=1.)</span></dt><dd><p>Learning rate shrinks the contribution of each regressor by
<code class="docutils literal notranslate"><span class="pre">learning_rate</span></code>. There is a trade-off between <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> and
<code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>.</p>
</dd>
<dt><strong>loss</strong><span class="classifier">{‘linear’, ‘square’, ‘exponential’}, optional (default=’linear’)</span></dt><dd><p>The loss function to use when updating the weights after each
boosting iteration.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>base_estimator_</strong><span class="classifier">estimator</span></dt><dd><p>The base estimator from which the ensemble is grown.</p>
</dd>
<dt><strong>estimators_</strong><span class="classifier">list of classifiers</span></dt><dd><p>The collection of fitted sub-estimators.</p>
</dd>
<dt><strong>estimator_weights_</strong><span class="classifier">array of floats</span></dt><dd><p>Weights for each estimator in the boosted ensemble.</p>
</dd>
<dt><strong>estimator_errors_</strong><span class="classifier">array of floats</span></dt><dd><p>Regression error for each estimator in the boosted ensemble.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.feature_importances_" title="sklearn.ensemble.AdaBoostRegressor.feature_importances_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_importances_</span></code></a><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>Return the feature importances (the higher, the more important the feature).</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AdaBoostClassifier</span></code></a>, <a class="reference internal" href="sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.tree.DecisionTreeRegressor</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r0c261b7dee9d-1"><span class="brackets">R0c261b7dee9d-1</span></dt>
<dd><p>Y. Freund, R. Schapire, “A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting”, 1995.</p>
</dd>
<dt class="label" id="r0c261b7dee9d-2"><span class="brackets">R0c261b7dee9d-2</span></dt>
<dd><ol class="upperalpha simple" start="8">
<li><p>Drucker, “Improving Regressors using Boosting Techniques”, 1997.</p></li>
</ol>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">AdaBoostRegressor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span>                       <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span> <span class="o">=</span> <span class="n">AdaBoostRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">AdaBoostRegressor(n_estimators=100, random_state=0)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">feature_importances_</span>
<span class="go">array([0.2788..., 0.7109..., 0.0065..., 0.0036...])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="go">array([4.7972...])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">0.9771...</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.fit" title="sklearn.ensemble.AdaBoostRegressor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Build a boosted regressor from the training set (X, y).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.get_params" title="sklearn.ensemble.AdaBoostRegressor.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.predict" title="sklearn.ensemble.AdaBoostRegressor.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict regression value for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.score" title="sklearn.ensemble.AdaBoostRegressor.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination R^2 of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.set_params" title="sklearn.ensemble.AdaBoostRegressor.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.staged_predict" title="sklearn.ensemble.AdaBoostRegressor.staged_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">staged_predict</span></code></a>(self, X)</p></td>
<td><p>Return staged predictions for X.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor.staged_score" title="sklearn.ensemble.AdaBoostRegressor.staged_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">staged_score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return staged scores for X, y.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">base_estimator=None</em>, <em class="sig-param">n_estimators=50</em>, <em class="sig-param">learning_rate=1.0</em>, <em class="sig-param">loss='linear'</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L952"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.feature_importances_">
<em class="property">property </em><code class="sig-name descname">feature_importances_</code><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.feature_importances_" title="Permalink to this definition">¶</a></dt>
<dd><dl class="simple">
<dt>Return the feature importances (the higher, the more important the</dt><dd><p>feature).</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>feature_importances_</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The feature importances.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L968"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Build a boosted regressor from the training set (X, y).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The target values (real numbers).</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, the sample weights are initialized to
1 / n_samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L1111"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict regression value for X.</p>
<p>The predicted regression value of an input sample is computed
as the weighted median prediction of the classifiers in the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The predicted regression values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a
precomputed kernel matrix or a list of generic objects instead,
shape = (n_samples, n_samples_fitted),
where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>R^2 of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The R2 score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor will use
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>. This will influence the
<code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>). To specify the
default value manually and avoid the warning, please either call
<a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a> directly or make a custom scorer with
<a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> (the built-in scorer <code class="docutils literal notranslate"><span class="pre">'r2'</span></code> uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code>).</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.staged_predict">
<code class="sig-name descname">staged_predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L1133"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Return staged predictions for X.</p>
<p>The predicted regression value of an input sample is computed
as the weighted median prediction of the classifiers in the ensemble.</p>
<p>This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">generator of array, shape = [n_samples]</span></dt><dd><p>The predicted regression values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.AdaBoostRegressor.staged_score">
<code class="sig-name descname">staged_score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_weight_boosting.py#L207"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return staged scores for X, y.</p>
<p>This generator method yields the ensemble score after each iteration of
boosting and therefore allows monitoring, such as to determine the
score on a test set after each boost.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>z</strong><span class="classifier">float</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-ensemble-adaboostregressor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostRegressor</span></code><a class="headerlink" href="#examples-using-sklearn-ensemble-adaboostregressor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_adaboost_regression_thumb.png" src="../../_images/sphx_glr_plot_adaboost_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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